“It’s only a gimmick.” When I heard myself saying this a few years ago about voice-activated assistants such as Alexa and Google Home, I almost bit my tongue afterward. This was because such arguments were almost always a sure sign of the coming market maturity of a technology, and I was usually accustomed to hearing this from insurance company directors who were well-advanced in their years. That had been the case with PCs, modem-based internet, mobile internet, social media and attention hacking.
However, despite the boom of useful and not-so-useful digitization initiatives, voice-activated technology is still in its early days. There are a whole series of voice skill products, for example by Aviva, LV and Travelers, but they were unable to appease the many critiques. Many people reacted as I did initially – seeing the products as gimmicks.
It is, of course, true that the number of daily users as well as the number of regularly used voice skills is still relatively low, although this was also true of smartphones 10 years ago. At that time, most people still used mobiles without a touchscreen. Many decision-makers also said that no customer would take out insurance using a mobile. They were wrong. Today, it’s normal that insurance policies are taken out online via smartphones or with apps. Why therefore shouldn’t this development also repeat itself in voice technology?
Smartphones and voice-activated technologies do, after all, have one thing in common: They help make work processes easier and faster. It is simply quicker and more convenient to take out insurance with Getsafe or Lemonade by app or with DFV or Ergo using Alexa.
We think that the insurance industry also has to look at selling insurance products via digital language assistants, which is highlighted in the study “Digital Insurance 2018,” conducted by Adcubum. This revealed that almost one in five Germans under 35 years old could envisage taking out an insurance policy using a digital language assistant. Additionally, according to a Statista survey of 2018, the number of people using digital assistants has more than doubled in three years. An increasing number of industries are also discovering the strategic benefits of voice technology – for example, car manufacturers with their own language assistants. Encouraged by this development, insurance companies should incorporate Alexa into their information and contract completion process as quickly as possible.
Until now, very few companies in the sector really offer a fully digitized insurance completion and consultation process, for example with Alexa. These include the insurtech company Deutsche Familienversicherung (DFV). The Frankfurt-based business programmed a skill in 2018 that conducts the consultation dialogue and accesses it via an API interface on the actuarial calculation engine. The skill evaluates the questions and provides the relevant product content, including monthly payments, voice-activated and in real time. In this case, integration in Amazon Echo goes beyond insurance contract completion and includes contract updating and payment handling, reducing call center and back office costs.
Alongside information, branding and sales, voice skill developers are also working on applications for the use of language assistants in the office, which appeals to brokerage firms. Case workers only need say, “Alexa, please inform the assessor for the Meyer case,” and brokers only need say, “Check status of billing.” This is significantly easier than the tedious battle with painfully complex user interfaces.
Even in the early stages of voice-activated technology and assistant systems, those investing in their development can secure an edge over the competition. Wouldn’t it be good to be among those present at the start of this technology of the future and gain market share? Let us make the most of these opportunities and not miss out on them, as was the case with PC, the internet and social media.
Are the Amazon Echo and Google Home insurtech? They sure are!
The two top-selling smart speakers have become so competitive that, in Canada during the past holiday season, each company undercut the other by offering the smaller versions, the Echo Dot and Google Home Mini, for as little as $39.99. For that price, why not buy one and try it out?
A recent report from Canalys states that the smart speaker is now the fastest-growing consumer technology. It is growing faster than augmented reality (AR), virtual reality (VR) and wearables, with smart speaker shipments expected to top 56 million units in 2018.
Since the launch in Canada of the Google Home in July 2017 and subsequently the Amazon Echo in December 2017, the following insurance services have been made available:
Aviva Canada — Aviva made a skill for the Amazon Echo to help consumers find answers to common insurance questions and to get an insurance quote. If a person is curious about accident benefits, for example, all they have to do is ask, “Alexa, what is accident benefits coverage?”
Manulife — Manulife’s skills for the Amazon Echo advise customers on what is left on their health benefits. Need new glasses but not sure how much coverage you have? Simply ask, “Alexa, ask Manulife Benefits how much do I have left for glasses?”
Kanetix.ca and InsuranceHotline.com — Both comparison websites made Google Assistant actions to support comparing car insurance quotes. Drivers just have to request a quote by saying, “Hey, Google, ask Kanetix.ca for a car insurance quote.”
RateSupermarket.ca has a Google Assistant action that supports finding the best mortgage rate in any province. All one has to do is say, “Hey, Google, ask RateSupermarket.ca for the best mortgage rate in Ontario.”
Not only is the smart speaker convenient for finding information, it is also spurring the sales of smart devices and IoT (Internet of Things) technology for the home such as smart plugs, smart appliances and smart entertainment…basically, smart everything. The Amazon Echo and Google Home can be used to turn on the lights, turn on the TV, change the channel and even find the best science fiction on Netflix.
Here is some recent data from ComScore and Statista showing the likelihood of IoT ownership for smart speaker households.
Google rarely has any presence at the Consumer Electronics Show, but this year Google is out in full force going head-on with Amazon Echo. Smart speaker adoption and integration to IoT devices is expected to be a megatrend at this year’s CES, which began yesterday in Las Vegas.
The adoption, sales and marketing of both Amazon and Google smart speaker assistants is clearly making this device a must-have in the home. Insurance providers cannot ignore this opportunity to develop smarter, more convenient ways to service their customers. If the smart speaker can really fire up IoT adoption in the home, insurance providers can’t ignore the data it can collect to create better products that improve the management of risk and claims for the household.
“Alexa, can you tell me the impact of the wholesale shift to voice search and voice communication over the internet?”
Amazon’s wildly popular personal assistant, Alexa, probably cannot answer that question for you. And even she doesn’t perceive how she is making us dumber and taking our choices away.
The world is surely moving from text to voice as the primary interface on the internet. The rapid rise of Amazon’s Echo (and its smaller version, the Echo Dot) personal assistant device was the biggest story of the 2016 holiday shopping season. As of September 2017, Amazon had sold 15 million Echos, and Google had sold five million of its own personal assistant device, the Google Home. This is impressive, for a category that just a year earlier had not existed.
Such growth has been enabled by dramatic improvements in voice recognition, through use of powerful artificial intelligence systems that use machine learning. We are now in a positive feedback loop for voice: As more people talk to their smartphones or home assistants, more data become available to companies such as Amazon, Google and Apple to feed to their personal assistant systems. As of May 2017, Google’s speech recognition error rate was 4.9%, down from 23% in 2013.
Businesses have recognized the shift in accuracy and customer engagement, and are piling in. Amazon now boasts more than 15,000 Alexa “skills,” which are capabilities that allow customers to make personalized requests. For example, travel search providers let you plan vacations via Alexa using voice commands; Pizza Hut lets you order pizza; Nissan and Hyundai let Alexa owners start their cars’ engines and set their temperatures; Capital One lets customers check their bank balances; and Campbell Soup Company supplies recipe ideas.
The shift to voice search and voice communication will surely make many things more convenient for us but will dramatically reduce our online choices. The reason for this is simple: When results are spoken back to us, we will receive only a few options, because humans cannot absorb 10 results in succession and adequately choose between them. We can’t remember them all. This switch in information density has profound implications, and voice search can subvert our purchasing choices in subtle ways.
Prior to the advent of the internet, when we looked at the Yellow Pages, we had many pages of options. When we searched online, we had even more options but tended to only react to those on the first page. Increasingly, those first-page results are sold to the highest bidder. On mobile phones, the searches mean even fewer options, and the paid ones utterly dominate the screen.
In the results of a voice search, we are usually down to only two or three options. People just can’t remember more information presented to them vocally. So your search for “best hotel in San Francisco” will yield only a few results. The response to “I want to find a pizza place in Palo Alto” might not show the pizza joint that is the best in town, because it has not bought its spot in the search results.
Most worryingly, the shift to voice will further consolidate power in the hands of the big providers, such as Amazon, Google and Apple.
When we ask Alexa to add olive oil to our shopping cart, we are ceding our choice to Amazon. Maybe we prefer Californian olive oil, because we know it is less likely to be adulterated. Or maybe we would rather buy the lower-priced of two favorite brands. With voice, which olive oil goes into the cart becomes Amazon’s decision. Unsurprisingly, research firm L2 found that Amazon is more likely to put its own proprietary products into your shopping cart.
In theory, we could ask for more voice results to get richer searches. Or perhaps voice assistant systems will eventually be improved to include capacities such as following up to ask us whether we want, for example, a particular type of pizza.
But even if that happens, the world of voice is taking us back a century in terms of information density. Talking to a voice assistant is a lot like asking a friend for restaurant recommendations, except that friend is a giant technology company that makes its money from the recommendations it provides us. That doesn’t sound very friendly.
This article was written by Vivek Wadhwa and Alex Salkever.
Jeff Heepke knows where to plant corn on his 4,500-acre farm in Illinois because of artificial intelligence (AI). He uses a smartphone app called Climate Basic, which divides Heepke’s farmland (and, in fact, the entire continental U.S.) into plots that are 10 meters square. The app draws on local temperature and erosion records, expected precipitation, soil quality and other agricultural data to determine how to maximize yields for each plot. If a rainy cold front is expected to pass by, Heepke knows which areas to avoid watering or irrigating that afternoon. As the U.S. Department of Agriculture noted, this use of artificial intelligence across the industry has produced the largest crops in the country’s history.
Climate Corp., the Silicon Valley–based developer of Climate Basic, also offers a more advanced AI app that operates autonomously. If a storm hits a region, or a drought occurs, it lowers local yield numbers. Farmers who have bought insurance to supplement their government coverage get a check; no questions asked, no paper filing necessary. The insurance companies and farmers both benefit from having a much less labor-intensive, more streamlined and less expensive automated claims process.
Monsanto paid nearly $1 billion to buy Climate Corp. in 2013, giving the company’s models added legitimacy. Since then, Monsanto has continued to upgrade the AI models, integrating data from farm equipment and sensors planted in the fields so that they improve their accuracy and insight as more data is fed into them. One result is a better understanding of climate change and its effects — for example, the northward migration of arable land for corn, or the increasing frequency of severe storms.
Applications like this are typical of the new wave of artificial intelligence in business. AI is generating new approaches to business models, operations and the deployment of people that are likely to fundamentally change the way business operates. And if it can transform an earthbound industry like agriculture, how long will it be before your company is affected?
An Unavoidable Opportunity
Many business leaders are keenly aware of the potential value of artificial intelligence but are not yet poised to take advantage of it. In PwC’s 2017 Digital IQ survey of senior executives worldwide, 54% of the respondents said they were making substantial investments in AI today. But only 20% said their organizations had the skills necessary to succeed with this technology (see “Winning with Digital Confidence,” by Chris Curran and Tom Puthiyamadam).
Reports on artificial intelligence tend to portray it as either a servant, making all technology more responsive, or an overlord, eliminating jobs and destroying privacy. But for business decision makers, AI is primarily an enabler of productivity. It will eliminate jobs, to be sure, but it will also fundamentally change work processes and might create jobs in the long run. The nature of decision making, collaboration, creative art and scientific research will all be affected; so will enterprise structures. Technological systems, including potentially your products and services, as well as your office and factory equipment, will respond to people (and one another) in ways that feel as if they are coming to life.
In their book Artificial Intelligence: A Modern Approach (Pearson, 1995), Stuart Russell and Peter Norvig define AI as “the designing and building of intelligent agents that receive percepts from the environment and take actions that affect that environment.” The most critical difference between AI and general-purpose software is in the phrase “take actions.” AI enables machines to respond on their own to signals from the world at large, signals that programmers do not directly control and therefore can’t anticipate.
The fastest-growing category of AI is machine learning, or the ability of software to improve its own activity by analyzing interactions with the world at large (see “The Road to Deep Learning,” below). This technology, which has been a continual force in the history of computing since the 1940s, has grown dramatically in sophistication during the last few years.
This may be the first moment in AI’s history when a majority of experts agree the technology has practical value. From its conceptual beginnings in the 1950s, led by legendary computer scientists such as Marvin Minsky and John McCarthy, its future viability has been the subject of fierce debate. As recently as 2000, the most proficient AI system was roughly comparable, in complexity, to the brain of a worm. Then, as high-bandwidth networking, cloud computing, and high-powered graphics-enabled microprocessors emerged, researchers began building multilayered neural networks — still extremely slow and limited in comparison with natural brains, but useful in practical ways.
The best-known AI triumphs — in which software systems beat expert human players in Jeopardy, chess, Go, poker and soccer — differ from most day-to-day business applications. These games have prescribed rules and well-defined outcomes; every game ends in a win, loss or tie. The games are also closed-loop systems: They affect only the players, not outsiders. The software can be trained through multiple failures with no serious risks. You can’t say the same of an autonomous vehicle crash, a factory failure or a mistranslation.
There are currently two main schools of thought on how to develop the inference capabilities necessary for AI programs to navigate through the complexities of everyday life. In both, programs learn from experience — that is, the responses and reactions they get influence the way the programs act thereafter. The first approach uses conditional instructions (also known as heuristics) to accomplish this. For instance, an AI bot would interpret the emotions in a conversation by following a program that instructed it to start by checking for emotions that were evident in the recent past.
The second approach is known as machine learning. The machine is taught, using specific examples, to make inferences about the world around it. It then builds its understanding through this inference-making ability, without following specific instructions to do so. The Google search engine’s “next-word completion” feature is a good example of machine learning. Type in the word artificial, and several suggestions for the next word will appear, perhaps intelligence, selection and insemination. No one has programmed the search engine to seek those complements. Google chose the strategy of looking for the three words most frequently typed after artificial. With huge amounts of data available, machine learning can provide uncanny accuracy about patterns of behavior.
The type of machine learning called deep learning has become increasingly important. A deep learning system is a multilayered neural network that learns representations of the world and stores them as a nested hierarchy of concepts many layers deep. For example, when processing thousands of images, it recognizes objects based on a hierarchy of simpler building blocks: straight lines and curved lines at the basic level, then eyes, mouths and noses, and then faces, and then specific facial features. Besides image recognition, deep learning appears to be a promising way to approach complex challenges such as speech comprehension, human-machine conversation, language translation and vehicle navigation (see Exhibit A).
Though it is the closest machine to a human brain, a deep learning neural network is not suitable for all problems. It requires multiple processors with enormous computing power, far beyond conventional IT architecture; it will learn only by processing enormous amounts of data; and its decision processes are not transparent.
News aggregation software, for example, had long relied on rudimentary AI to curate articles based on people’s requests. Then it evolved to analyze behavior, tracking the way people clicked on articles and the time they spent reading, and adjusting the selections accordingly. Next it aggregated individual users’ behavior with the larger population, particularly those who had similar media habits. Now it is incorporating broader data about the way readers’ interests change over time, to anticipate what people are likely to want to see next, even if they have never clicked on that topic before. Tomorrow’s AI aggregators will be able to detect and counter “fake news” by scanning for inconsistencies and routing people to alternative perspectives.
AI applications in daily use include all smartphone digital assistants, email programs that sort entries by importance, voice recognition systems, image recognition apps such as Facebook Picture Search, digital assistants such as Amazon Echo and Google Home and much of the emerging Industrial Internet. Some AI apps are targeted at minor frustrations — DoNotPay, an online legal bot, has reversed thousands of parking tickets — and others, such as connected car and language translation technologies, represent fundamental shifts in the way people live. A growing number are aimed at improving human behavior; for instance, GM’s 2016 Chevrolet Malibu feeds data from sensors into a backseat driver–like guidance system for teenagers at the wheel.
Despite all this activity, the market for AI is still small. Market research firm Tractica estimated 2016 revenues at just $644 million. But it expects hockey stick-style growth, reaching $15 billion by 2022 and accelerating thereafter. In late 2016, there were about 1,500 AI-related startups in the U.S. alone, and total funding in 2016 reached a record $5 billion. Google, Facebook, Microsoft, Salesforce.com and other tech companies are snapping up AI software companies, and large, established companies are recruiting deep learning talent and, like Monsanto, buying AI companies specializing in their markets. To make the most of this technology in your enterprise, consider the three main ways that businesses can or will use AI:
Assisted intelligence, now widely available, improves what people and organizations are already doing.
Augmented intelligence, emerging today, enables organizations and people to do things they couldn’t otherwise do.
Autonomous intelligence, being developed for the future, creates and deploys machines that act on their own.
Many companies will make investments in all three during the next few years, drawing from a wide variety of applications (see Exhibit 1). They complement one another but require different types of investment, different staffing considerations and different business models.
Assisted intelligence amplifies the value of existing activity. For example, Google’s Gmail sorts incoming email into “Primary,” “Social” and “Promotion” default tabs. The algorithm, trained with data from millions of other users’ emails, makes people more efficient without changing the way they use email or altering the value it provides.
Assisted intelligence tends to involve clearly defined, rules-based, repeatable tasks. These include automated assembly lines and other uses of physical robots; robotic process automation, in which software-based agents simulate the online activities of a human being; and back-office functions such as billing, finance and regulatory compliance. This form of AI can be used to verify and cross-check data — for example, when paper checks are read and verified by a bank’s ATM. Assisted intelligence has already become common in some enterprise software processes. In “opportunity to order” (basic sales) and “order to cash” (receiving and processing customer orders), the software offers guidance and direction that was formerly available only from people.
The Oscar W. Larson Co. used assisted intelligence to improve its field service operations. This is a 70-plus-year-old family-owned general contractor, which, among other services to the oil and gas industry, provides maintenance and repair for point-of-sales systems and fuel dispensers at gas stations. One costly and irritating problem is “truck rerolls”: service calls that have to be rescheduled because the technician lacks the tools, parts or expertise for a particular issue. After analyzing data on service calls, the AI software showed how to reduce truck rerolls by 20%, a rate that should continue to improve as the software learns to recognize more patterns.
Assisted intelligence apps often involve computer models of complex realities that allow businesses to test decisions with less risk. For example, one auto manufacturer has developed a simulation of consumer behavior, incorporating data about the types of trips people make, the ways those affect supply and demand for motor vehicles and the variations in those patterns for different city topologies, marketing approaches and vehicle price ranges. The model spells out more than 200,000 variations for the automaker to consider and simulates the potential success of any tested variation, thus assisting in the design of car launches. As the automaker introduces cars and the simulator incorporates the data on outcomes from each launch, the model’s predictions will become ever more accurate.
AI-based packages of this sort are available on more and more enterprise software platforms. Success with assisted intelligence should lead to improvements in conventional business metrics such as labor productivity, revenues or margins per employee and average time to completion for processes. Much of the cost involved is in the staff you hire, who must be skilled at marshaling and interpreting data. To evaluate where to deploy assisted intelligence, consider two questions: What products or services could you easily make more marketable if they were more automatically responsive to your customers? Which of your current processes and practices, including your decision-making practices, would be more powerful with more intelligence?
Augmented intelligence software lends new capability to human activity, permitting enterprises to do things they couldn’t do before. Unlike assisted intelligence, it fundamentally alters the nature of the task, and business models change accordingly.
For example, Netflix uses machine learning algorithms to do something media has never done before: suggest choices customers would probably not have found themselves, based not just on the customer’s patterns of behavior but on those of the audience at large. A Netflix user, unlike a cable TV pay-per-view customer, can easily switch from one premium video to another without penalty, after just a few minutes. This gives consumers more control over their time. They use it to choose videos more tailored to the way they feel at any given moment. Every time that happens, the system records that observation and adjusts its recommendation list — and it enables Netflix to tailor its next round of videos to user preferences more accurately. This leads to reduced costs and higher profits per movie, and a more enthusiastic audience, which then enables more investments in personalization (and AI). Left outside this virtuous circle are conventional advertising and television networks. No wonder other video channels, such as HBO and Amazon, as well as recorded music channels such as Spotify, have moved to similar models.
Over time, as algorithms grow more sophisticated, the symbiotic relationship between human and AI will further change entertainment industry practices. The unit of viewing decision will probably become the scene, not the story; algorithms will link scenes to audience emotions. A consumer might ask to see only scenes where a Meryl Streep character is falling in love, or to trace a particular type of swordplay from one action movie to another. Data accumulating from these choices will further refine the ability of the entertainment industry to spark people’s emotions, satisfy their curiosity and gain their loyalty.
Another current use of augmented intelligence is in legal research. Though most cases are searchable online, finding relevant precedents still requires many hours of sifting through past opinions. Luminance, a startup specializing in legal research, can run through thousands of cases in a very short time, providing inferences about their relevance to a current proceeding. Systems like these don’t yet replace human legal research. But they dramatically reduce the rote work conducted by associate attorneys, a job rated as the least satisfying in the U.S. Similar applications are emerging for other types of data sifting, including financial audits, interpreting regulations, finding patterns in epidemiological data and (as noted above) farming.
To develop applications like these, you’ll need to marshal your own imagination to look for products, services or processes that would not be possible at all without AI. For example, an AI system can track a wide number of product features, warranty costs, repeat purchase rates and more general purchasing metrics, bringing only unusual or noteworthy correlations to your attention. Are a high number of repairs associated with a particular region, material or line of products? Could you use this information to redesign your products, avoid recalls or spark innovation in some way?
The success of an augmented intelligence effort depends on whether it has enabled your company to do new things. To assess this capability, track your margins, innovation cycles, customer experience and revenue growth as potential proxies. Also watch your impact on disruption: Are your new innovations doing to some part of the business ecosystem what, say, ride-hailing services are doing to conventional taxi companies?
You won’t find many off-the-shelf applications for augmented intelligence. They involve advanced forms of machine learning and natural language processing, plus specialized interfaces tailored to your company and industry. However, you can build bespoke augmented intelligence applications on cloud-based enterprise platforms, most of which allow modifications in open source code. Given the unstructured nature of your most critical decision processes, an augmented intelligence application would require voluminous historical data from your own company, along with data from the rest of your industry and related fields (such as demographics). This will help the system distinguish external factors, such as competition and economic conditions, from the impact of your own decisions.
The greatest change from augmented intelligence may be felt by senior decision makers, as the new models often give them new alternatives to consider that don’t match their past experience or gut feelings. They should be open to those alternatives, but also skeptical. AI systems are not infallible; just like any human guide, they must show consistency, explain their decisions and counter biases, or they will lose their value.
Very few autonomous intelligence systems — systems that make decisions without direct human involvement or oversight — are in widespread use today. Early examples include automated trading in the stock market (about 75% of Nasdaq trading is conducted autonomously) and facial recognition. In some circumstances, algorithms are better than people at identifying other people. Other early examples include robots that dispose of bombs, gather deep-sea data, maintain space stations and perform other tasks inherently unsafe for people.
The most eagerly anticipated forms of autonomous intelligence — self-driving cars and full-fledged language translation programs — are not yet ready for general use. The closest autonomous service so far is Tencent’s messaging and social media platform WeChat, which has close to 800 million daily active users, most of them in China. The program, which was designed primarily for use on smartphones, offers relatively sophisticated voice recognition, Chinese-to-English language translation, facial recognition (including suggestions of celebrities who look like the person holding the phone) and virtual bot friends that can play guessing games. Notwithstanding their cleverness and their pioneering use of natural language processing, these are still niche applications, and still very limited by technology. Some of the most popular AI apps, for example, are small, menu- and rule-driven programs, which conduct fairly rudimentary conversations around a limited group of options.
Despite the lead time required to bring the technology further along, any business prepared to base a strategy on advanced digital technology should be thinking seriously about autonomous intelligence now. The Internet of Things will generate vast amounts of information, more than humans can reasonably interpret. In commercial aircraft, for example, so much flight data is gathered that engineers can’t process it all; thus, Boeing has announced a $7.5 million partnership with Carnegie Mellon University, along with other efforts to develop AI systems that can, for example, predict when airplanes will need maintenance. Autonomous intelligence’s greatest challenge may not be technological at all — it may be companies’ ability to build in enough transparency for people to trust these systems to act in their best interest.
As you contemplate the introduction of artificial intelligence, articulate what mix of the three approaches works best for you.
Are you primarily interested in upgrading your existing processes, reducing costs and improving productivity? If so, then start with assisted intelligence, probably with a small group of services from a cloud-based provider.
Do you seek to build your business around something new — responsive and self-driven products, or services and experiences that incorporate AI? Then pursue an augmented intelligence approach, probably with more complex AI applications resident on the cloud.
Are you developing a genuinely new technology? Most companies will be better off primarily using someone else’s AI platforms, but, if you can justify building your own, you may become one of the leaders in your market.
The transition among these forms of AI is not clean-cut; they sit on a continuum. In developing their own AI strategy, many companies begin somewhere between assisted and augmented, while expecting to move toward autonomous eventually (see Exhibit 2).
Though investments in AI may seem expensive now, the costs will decline over the next 10 years as the software becomes more commoditized. “As this technology continues to mature,” writes Daniel Eckert, a managing director in emerging technology services for PwC US, “we should see the price adhere toward a utility model and flatten out. We expect a tiered pricing model to be introduced: a free (or freemium model) for simple activities, and a premium model for discrete, business-differentiating services.”
AI is often sold on the premise that it will replace human labor at lower cost — and the effect on employment could be devastating, though no one knows for sure. Carl Benedikt Frey and Michael Osborne of Oxford University’s engineering school have calculated that AI will put 47% of the jobs in the U.S. at risk; a 2016 Forrester research report estimated it at 6%, at least by 2025. On the other hand, Baidu Research head (and deep learning pioneer) Andrew Ng recently said, “AI is the new electricity,” meaning that it will be found everywhere and create jobs that weren’t imaginable before its appearance.
At the same time that AI threatens the loss of an almost unimaginable number of jobs, it is also a hungry, unsatisfied employer. The lack of capable talent — people skilled in deep learning technology and analytics — may well turn out to be the biggest obstacle for large companies. The greatest opportunities may thus be for independent businesspeople, including farmers like Jeff Heepke, who no longer need scale to compete with large companies, because AI has leveled the playing field.
It is still too early to say which types of companies will be the most successful in this area — and we don’t yet have an AI model to predict it for us. In the end, we cannot even say for sure that the companies that enter the field first will be the most successful. The dominant players will be those that, like Climate Corp., Oscar W. Larson, Netflix and many other companies large and small, have taken AI to heart as a way to become far more capable, in a far more relevant way, than they otherwise would ever be.
Artificial intelligence (AI) is advancing so rapidly that even its developers are being caught off guard. Google co-founder Sergey Brinsaid in Davos, Switzerland,in January that it “touches every single one of our main projects, ranging from search to photos to ads … everything we do … it definitely surprised me, even though I was sitting right there.”
Thelong-promised AI, the stuff we’ve seen in science fiction, is coming, and we need to be prepared. Today, AI is powering voice assistants such as Google Home, Amazon Alexa and Apple Siri, allowing them to have increasingly natural conversations with us and manage our lights, order food and schedule meetings. Businesses are infusing AI into their products to analyze the vast amounts of data and improve decision-making. In a decade or two, we will have robotic assistants that remind us of Rosie from “The Jetsons” and R2-D2 of “Star Wars.”
This has profound implications for how we live and work, for better and worse. AI is going to become our guide and companion — and take millions ofjobs away from people. We can deny this is happening, be angry or simply ignore it. But, if we do, we will be the losers. As I discussed in my new book, “Driver in the Driverless Car,” technology is now advancing on an exponential curve and making science fiction a reality. We can’t stop it. All we can do is to understand it and use it to better ourselves — and humanity.
Rosie and R2-D2 may be on their way, but AI is still very limited in its capability, and will be for a long time. The voice assistants are examples of what technologists call narrow AI: systems that are useful, can interact with humans and bear some of the hallmarks of intelligence — but would never be mistaken for a human. They can, however, do a better job on a very specific range of tasks than humans can. I couldn’t, for example, recall the winning and losing pitcher in every baseball game of the major leagues from the previous night.
Narrow-AI systems are much better than humans at accessing information stored in complex databases, but their capabilities exclude creative thought. If you asked Siri to find the perfect gift for your mother for Valentine’s Day, Siri might make a snarky comment but couldn’t venture an educated guess. If you asked her to write your term paper on the Napoleonic Wars, she couldn’t help. That is where the human element comes in and where the opportunities are for us to benefit from AI — and stay employed.
In his book “Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins,” chess grandmaster Garry Kasparov tells of his shock and anger at being defeated by IBM’s Deep Blue supercomputer in 1997. He acknowledges that he is a sore loser but was clearly traumatized by having a machine outsmart him. He was aware of the evolution of the technology but never believed it would beat him at his own game. After coming to grips with his defeat, 20 years later, he says fail-safes are required … but so is courage.
Kasparov wrote: “When I sat across from Deep Blue 20 years ago, I sensed something new, something unsettling. Perhaps you will experience a similar feeling the first time you ride in a driverless car, or the first time your new computer boss issues an order at work. We must face these fears in order to get the most out of our technology and to get the most out of ourselves. Intelligent machines will continue that process, taking over the more menial aspects of cognition and elevating our mental lives toward creativity, curiosity, beauty and joy. These are what truly make us human, not any particular activity or skill like swinging a hammer — or even playing chess.”
In other words, we better get used to AI and ride the wave.
Human superiority over animals is based on our ability to create and use tools. The mental capacity to make things that improved our chances of survival led to a natural selection of better toolmakers and tool users. Nearly everything a human does involves technology. For adding numbers, we used abacuses and mechanical calculators and now have spreadsheets. To improve our memory, we wrote on stones, parchment and paper, and now have disk drives and cloud storage.
AI is the next step in improving our cognitive functions and decision-making.
Think about it: When was the last time you tried memorizing your calendar or Rolodex or used a printed map? Just as we instinctively do everything on our smartphones, we will rely on AI. We may have forfeited skills such as the ability to add up the price of our groceries, but we are smarter and more productive. With the help of Google and Wikipedia, we can be experts on any topic, and these don’t make us any dumber than encyclopedias, phone books and librarians did.
A valid concern is that dependence on AI may cause us to forfeit human creativity. As Kasparov observes, the chess games on our smartphones are many times more powerful than the supercomputers that defeated him, yet this didn’t cause human chess players to become less capable — the opposite happened. There are now stronger chess players all over the world, and the game is played in a better way.
As Kasparov explains: “It used to be that young players might acquire the style of their early coaches. If you worked with a coach who preferred sharp openings and speculative attacking play himself, it would influence his pupils to play similarly. … What happens when the early influential coach is a computer? The machine doesn’t care about style or patterns or hundreds of years of established theory. It counts up the values of the chess pieces, analyzes a few billion moves, and counts them up again. It is entirely free of prejudice and doctrine. … The heavy use of computers for practice and analysis has contributed to the development of a generation of players who are almost as free of dogma as the machines with which they train.”
Perhaps this is the greatest benefit that AI will bring — humanity can be free of dogma and historical bias; it can do more intelligent decision-making. And instead of doing repetitive data analysis and number crunching, human workers can focus on enhancing their knowledge and being more creative.